The invention belongs to the technical field of medical 
image processing and 
computer vision, and particularly relates to an 
MRI image liver fibrosis automatic grading method based on imaging 
omics analysis. The invention particularly relates to an imaging 
omics analysis method for grading magnetic 
resonance dynamic enhanced imaging 
liver fibrosis of a Gd-EOB-DTPA contrast agent. The method comprises the following steps: establishing an initial 
data set by taking a Gd-EOB-DTPA dynamic enhanced image of a 
hepatitis B patient as 
source data; registering the initial data; carrying out automatic liver segmentation by utilizing transfer learning to obtain an ROI; performing image 
omics analysis: for the ROI, carrying out image omics 
feature extraction and 
feature screening on a plurality of DCEsequences to obtain a feature subset of important features, and carrying out training of a plurality of classifiers to select an optimal classifier; and finally, predicting and evaluating the classification performance in an external 
test set by using the classifier. According to the method, the accuracy and reliability of automatic 
liver fibrosis grading based on DCE images can be effectively improved, and meanwhile, the time and energy of clinicians are saved to a great extent.